Mathematical Statistics with Applications
7th Edition
ISBN: 9780495110811
Author: Dennis Wackerly, William Mendenhall, Richard L. Scheaffer
Publisher: Cengage Learning
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Question
Chapter 11.14, Problem 83E
a.
To determine
Check the appropriate test of hypothesis at the 0.05 level of significance.
b.
To determine
Compute the smallest value of
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
A linear regression model has been estimated for the variables Y="monthly consumption of veal (kg)", X1="monthly monetary household income (thousand EUR)" and X2="household size (number of members)" using data for a random sample of 80 households. The following results have been obtained:
b0=0.3 b1=0.5 b2=0.7 R-sq=0.9 R=0.95，Interpret the value of regression coefficient b2.
A linear regression equation has b= 3 and a =– 6. What is the predicted value of Y for X= 4?
A linear regression equation has b = 2 and a = 3.
What is the predicted value of Y for X = 8?
a. Y = 5
b. Y = 19
c. Y = 26
d. Cannot be determined without additional information
Chapter 11 Solutions
Mathematical Statistics with Applications
Ch. 11.3 - If 0 and 1 are the least-squares estimates for the...Ch. 11.3 - Prob. 2ECh. 11.3 - Fit a straight line to the five data points in the...Ch. 11.3 - Auditors are often required to compare the audited...Ch. 11.3 - Prob. 5ECh. 11.3 - Applet Exercise Refer to Exercises 11.2 and 11.5....Ch. 11.3 - Prob. 7ECh. 11.3 - Laboratory experiments designed to measure LC50...Ch. 11.3 - Prob. 9ECh. 11.3 - Suppose that we have postulated the model...
Ch. 11.3 - Some data obtained by C.E. Marcellari on the...Ch. 11.3 - Processors usually preserve cucumbers by...Ch. 11.3 - J. H. Matis and T. E. Wehrly report the following...Ch. 11.4 - a Derive the following identity:...Ch. 11.4 - An experiment was conducted to observe the effect...Ch. 11.4 - Prob. 17ECh. 11.4 - Prob. 18ECh. 11.4 - A study was conducted to determine the effects of...Ch. 11.4 - Suppose that Y1, Y2,,Yn are independent normal...Ch. 11.4 - Under the assumptions of Exercise 11.20, find...Ch. 11.4 - Prob. 22ECh. 11.5 - Use the properties of the least-squares estimators...Ch. 11.5 - Do the data in Exercise 11.19 present sufficient...Ch. 11.5 - Use the properties of the least-squares estimators...Ch. 11.5 - Let Y1, Y2, . . . , Yn be as given in Exercise...Ch. 11.5 - Prob. 30ECh. 11.5 - Using a chemical procedure called differential...Ch. 11.5 - Prob. 32ECh. 11.5 - Prob. 33ECh. 11.5 - Prob. 34ECh. 11.6 - For the simple linear regression model Y = 0 + 1x...Ch. 11.6 - Prob. 36ECh. 11.6 - Using the model fit to the data of Exercise 11.8,...Ch. 11.6 - Refer to Exercise 11.3. Find a 90% confidence...Ch. 11.6 - Refer to Exercise 11.16. Find a 95% confidence...Ch. 11.6 - Refer to Exercise 11.14. Find a 90% confidence...Ch. 11.6 - Prob. 41ECh. 11.7 - Suppose that the model Y=0+1+ is fit to the n data...Ch. 11.7 - Prob. 43ECh. 11.7 - Prob. 44ECh. 11.7 - Prob. 45ECh. 11.7 - Refer to Exercise 11.16. Find a 95% prediction...Ch. 11.7 - Refer to Exercise 11.14. Find a 95% prediction...Ch. 11.8 - The accompanying table gives the peak power load...Ch. 11.8 - Prob. 49ECh. 11.8 - Prob. 50ECh. 11.8 - Prob. 51ECh. 11.8 - Prob. 52ECh. 11.8 - Prob. 54ECh. 11.8 - Prob. 55ECh. 11.8 - Prob. 57ECh. 11.8 - Prob. 58ECh. 11.8 - Prob. 59ECh. 11.8 - Prob. 60ECh. 11.9 - Refer to Example 11.10. Find a 90% prediction...Ch. 11.9 - Prob. 62ECh. 11.9 - Prob. 63ECh. 11.9 - Prob. 64ECh. 11.9 - Prob. 65ECh. 11.10 - Refer to Exercise 11.3. Fit the model suggested...Ch. 11.10 - Prob. 67ECh. 11.10 - Fit the quadratic model Y=0+1x+2x2+ to the data...Ch. 11.10 - The manufacturer of Lexus automobiles has steadily...Ch. 11.10 - a Calculate SSE and S2 for Exercise 11.4. Use the...Ch. 11.12 - Consider the general linear model...Ch. 11.12 - Prob. 72ECh. 11.12 - Prob. 73ECh. 11.12 - An experiment was conducted to investigate the...Ch. 11.12 - Prob. 75ECh. 11.12 - The results that follow were obtained from an...Ch. 11.13 - Prob. 77ECh. 11.13 - Prob. 78ECh. 11.13 - Prob. 79ECh. 11.14 - Prob. 80ECh. 11.14 - Prob. 81ECh. 11.14 - Prob. 82ECh. 11.14 - Prob. 83ECh. 11.14 - Prob. 84ECh. 11.14 - Prob. 85ECh. 11.14 - Prob. 86ECh. 11.14 - Prob. 87ECh. 11.14 - Prob. 88ECh. 11.14 - Refer to the three models given in Exercise 11.88....Ch. 11.14 - Prob. 90ECh. 11.14 - Prob. 91ECh. 11.14 - Prob. 92ECh. 11.14 - Prob. 93ECh. 11.14 - Prob. 94ECh. 11 - At temperatures approaching absolute zero (273C),...Ch. 11 - A study was conducted to determine whether a...Ch. 11 - Prob. 97SECh. 11 - Prob. 98SECh. 11 - Prob. 99SECh. 11 - Prob. 100SECh. 11 - Prob. 102SECh. 11 - Prob. 103SECh. 11 - An experiment was conducted to determine the...Ch. 11 - Prob. 105SECh. 11 - Prob. 106SECh. 11 - Prob. 107SE
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardA marketing analysit is studying the relashionship between X = money spent on television advertising and Y = increase in slae. A simple linear regression model relates x and y as follows Y = 27.5 + 1.19 X What is the average change in sales associated with an additional 1 dollor spent on advertising? Group of answer choices A. For every additional 1 dollor spent on advertising, sales decreases by 1.19 dollars. B. For every additional 1 dollor spent on advertising, sales increase by 1.19 dollars. C. For every additional 1 dollor spent on advertising, sales increase by 28.69 dollars. D. For every additional 1 dollor spent on advertising, sales decreases by 28.69 dollars. E. For every additional 1 dollor spent on advertising, sales increase by 27.5 dollars.arrow_forwardFour pairs of data yield an r = .902 and a regression equation of y = 4x + 7. Also the average y-value is 12.45. What is the best predicted value for y if x = 3.8 ? X 2 3 5 6 Y 4 9 14 20arrow_forward
- A multiple regression model was fit to predict domestic gross for movies based off of the production budget, opening weekend gross and number of stars. The overall F test p-value equals 0.252. What conclusion can you make at alpha = 0.05? All of the variables contribute to the model. At least one of the variables contributes to the model. O None of the variables contribute to the model.arrow_forwardIn a study, data collected for 40 observations were used to model the dependent variable y with 8 independent variables under consideration, x1, X2, ... , xg. Consider a stepwise regression run on the data. 1. What is the form of the model fit in step 1? How many models are fit? How is the "best" independent variable selected in this step? 2. What is the form of the model fit in step 2? How many models are fit? How is the "best" independent variable selected in this step? 3. What is the form of the model fit in step 3? How many mądels are fit? How is the "best" independent variable selected in this step? 4. The variables x3, x4, and x6 were deemed the best variables for predicting y. How do you recommend the researchers proceed from here? Why?arrow_forwardA multiple linear regression model based on a sample of 13 weeks is developed to predict standby hours based on the total staff present and remote hours. The SSR is 23,638.17 and the SSE is 33,273.99. a. Determine whether there is a significant relationship between standby hours and the two independent variables (total staff present and remote hours) at the 0.05 level of significance. What are the correct hypotheses to test?arrow_forward
- A company studying the productivity of its employees on a new information system was interested in determingg if the age (X) of data entry opeertors influenced the number of completed entries made per hour (Y). The regression equation is y = 14.374 - 0.145x Suppose the acyual completed entries per hour for an operator who is 35 years old was 8. The residual is:arrow_forwardUse the given data to find the best predicted value of the value of y. Use a significance level of 0.05.Six pairs of data yield r = 0.789 and the regression equation y=4x−2. Also, y¯=22. What is the best predicted value of y for x=5?arrow_forwardBxi + €i, where €; are independently and Consider a simple linear regression model Y; = identically distributed with mean 0 and variance o?, and i = 1,..., n. Note: this model does not have a intercept term. Derive the Best Linear Unbiased Estimator (BLUE) for B. Denote this by BBLUE. Make sure to state why this is the BLUE.arrow_forward
- Part A: find R and the regression equation, then use Table I for the critical value. Let x = Weight and y = Handling. Test at the .05 significance level. 1: Null and Alternative hypotheses 2: Critical score 3: Test score 4: Decision. Part B: If there is a strong linear correlation, do the following. 1. State the regression equation using variables x and y. 2. State the regression equation using Weight and Handling instead of x and y.arrow_forwardresearcher would like to predict the dependent variable YY from the two independent variables X1X1 and X2X2 for a sample of N=20N=20 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test the significance of the overall regression model. Use a significance level α=0.05α=0.05. X1X1 X2X2 YY 33.2 35.8 45.7 25.1 47.7 41.9 86.6 54.4 89.5 55.8 51.3 76.8 65.8 60.5 40.9 26.9 50.1 44.7 67.5 58.1 61.7 35.9 61.9 20 48.8 44.8 52.8 47.9 42.7 44.9 43.4 56.6 15.4 50.9 43.7 53.9 51.3 46 79.8 39.8 55.1 56 33 56.1 30.6 45.9 55 47.6 19.4 50.9 40.4 74.7 45.8 84.6 62.3 58.9 86.5 64.2 64.1 49.2 SSreg=SSreg= SSres=SSres= R2=R2= F=F= P-value = What is your decision for the hypothesis test? Reject the null hypothesis, H0:β1=β2=0H0:β1=β2=0 Fail to reject H0H0 What is your final conclusion? The evidence supports the claim that one or more of the regression coefficients is non-zero The evidence supports the…arrow_forwardA linear regression equation has b = 3 and a = – 6. What is the predicted value of Y for X = 4? a. Ŷ = 6 b. Ŷ = –21 c. Ŷ = –2 d. Cannot be determined without additional informationarrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning
Calculus For The Life SciencesCalculusISBN:9780321964038Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.Publisher:Pearson Addison Wesley,
Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw Hill
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Calculus For The Life Sciences
Calculus
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:Pearson Addison Wesley,
Glencoe Algebra 1, Student Edition, 9780079039897...
Algebra
ISBN:9780079039897
Author:Carter
Publisher:McGraw Hill
Correlation Vs Regression: Difference Between them with definition & Comparison Chart; Author: Key Differences;https://www.youtube.com/watch?v=Ou2QGSJVd0U;License: Standard YouTube License, CC-BY
Correlation and Regression: Concepts with Illustrative examples; Author: LEARN & APPLY : Lean and Six Sigma;https://www.youtube.com/watch?v=xTpHD5WLuoA;License: Standard YouTube License, CC-BY